Information processing method and terminal device

ABSTRACT

Disclosed are an information processing method and a terminal device. The method comprises: acquiring first information, wherein the first information is information to be processed by a terminal device, calling an operation instruction in a calculation apparatus to calculate the first information so as to obtain second information, and outputting the second information. By means of the examples in the present disclosure, a calculation apparatus of a terminal device can be used to call an operation instruction to process first information, so as to output second information of a target desired by a user, thereby improving the information processing efficiency. The present technical solution has advantages of a fast computation speed and high efficiency.

TECHNICAL FIELD

The present disclosure relates to the technical field of information technology, and particularly to an information processing method and related products.

BACKGROUND

With the growing information technology and people's ever-increasing demand, the need for timeliness of information becomes stronger. At present, a terminal obtains and processes information based on a general-purpose processor, such as running a specified application in a general-purpose processor to realize product recommendation for users, etc.

However, in practical applications, this way of obtaining information by a general-purpose processor running a software program may be limited by the operating speed of the general-purpose processor, and in particular, when the general-purpose processor has a large load, the efficiency of obtaining information may be low and the delay may be long.

SUMMARY

Examples of the present disclosure provide an information computation method and related products, which can increase processing speed and efficiency of a computation device.

In a first aspect, an example of the present disclosure provides an information processing method which is applied to a computation device, where the computation device includes a communication unit and an operation unit. The method includes:

controlling, by the computation device, the communication unit to obtain user data to be processed, where the user data is used to recommend product recommendation information of interest to the user;

controlling, by the computation device, the operation unit to obtain and call an operation instruction to process the user data to obtain product recommendation information corresponding to the user data, wherein the operation instruction is a preset instruction for product prediction and recommendation, and the product recommendation information includes at least one target product recommended for the user.

In some possible examples, the user data includes at least one of the following: user behavior data, user physical sign data, group data, and time data, where the user behavior data is used to indicate historical product records of the user, the user's physical sign data is used to indicate physiological data of the user, the group data is used to indicate data of other users within a same geographic area as the user, and the time data is used to indicate time of generation of the user behavior data.

In some possible examples, the computation device further includes a register unit and a controller unit, and the controlling, by the computation device, the operation unit to obtain and call an operation instruction to process the user data to obtain product recommendation information corresponding to the user data includes:

controlling, by the computation device, the controller unit to fetch an operation instruction associated with a network model from the register unit, and sending, by the computation device, the operation instruction to the operation unit; and

controlling, by the computation device, the operation unit to call an operation instruction associated with the network model to perform feature extraction on the user data to obtain respective recommendation information of a plurality of products, where the recommendation information at least includes a score of the product; and

controlling, by the computation device, the operation unit to select recommendation information whose score exceeds a preset threshold from the plurality of recommendation information as the product recommendation information.

In some possible examples, the network model includes any one or more of the following functional layers: a convolution operation layer, a pooling layer, an activation softmax layer, a batch normalization layer, and a fully connected layer; where the function layers are composed of at least one pre-stored operation instruction.

In some possible examples, the functional layers composing the neural network model, a count of the functional layers, and an order of the functional layers are customized by a user side or a terminal side.

In some possible examples, the user data includes sparse user data which is used to indicate the user's record data of which an amount is small.

In some possible examples, the computation device further includes a data access unit and a storage medium,

the computation device controls the operation unit to send the product recommendation information to the data access unit and store the product recommendation information in the storage medium.

In some possible examples, the operation unit includes a primary operation module and a plurality of secondary operation modules, where the primary operation module is interconnected with the plurality of secondary operation modules by an interconnection module, and when the operation instruction is a convolution operation instruction,

the calling the operation instruction to process the user data includes:

controlling, by the computation device, the secondary operation modules to implement a convolution operation of input data and a convolution kernel in a convolutional neural network algorithm, where the input data is the user data and the convolutional neural network algorithm corresponds to the convolution operation instruction,

controlling, by the computation device, the interconnection module to implement data transfer between the primary operation module and the secondary operation modules, before a forward operation of a neural network fully connected layer starts, transferring, by the primary operation module, the input data to each secondary operation modules through the interconnection module; and after the computation of the secondary operation modules is completed, splicing, by the interconnection module, output scalars of the respective secondary operation modules stage by stage to obtain an intermediate vector, and sending the intermediate vector back to the primary operation module, and

controlling, by the computation device, the primary operation module to splice intermediate vectors corresponding of all input data into an intermediate result for subsequent operations.

In some possible examples, the performing subsequent operations on the intermediate result includes:

controlling, by the computation device, the primary operation module to add bias data to the intermediate result, and then performing an activation operation.

In some possible examples, the primary operation module includes a first operation unit, where the first operation unit includes a vector addition unit and an activation unit,

the step of controlling, by the computation device, the primary operation module to add bias data to the intermediate result, and then performing an activation operation includes:

controlling, by the computation device, the vector addition unit to implement a bias addition operation of a convolutional neural network operation and perform element-wise addition on bias data and the intermediate result to obtain a bias result; and

controlling, by the computation device, the activation unit to perform an activation function operation on the bias result.

In some possible examples, the primary operation module includes a first storage unit, a first operation unit, a first data dependency determination unit, and a first storage unit. The above method further includes:

controlling, by the computation device, the first storage unit to cache input data and output data used by the primary operation module during a computation process, where the output data includes respective recommendation information of the plurality of products and/or the product recommendation information;

controlling, by the computation device, the first operation unit to perform various operational functions of the primary operation module;

controlling, by the computation device, the data dependency determination unit to ensure that there is no consistency conflict in reading data from and writing data to the first storage unit, read an input neuron vector from the first storage unit, and send the vector to the secondary operation modules through the interconnection module; and

sending an intermediate result vector from the interconnection module to the first operation unit.

In some possible examples, each secondary operation module includes a second operation unit, where the second operation unit includes a vector multiplication unit and an accumulation unit,

the controlling, by the computation device, the secondary operation modules to perform a convolution operation of input data and a convolution kernel in a convolutional neural network algorithm includes:

controlling, by the computation device, the vector multiplication unit to perform a vector multiplication operation of the convolution operation, and

controlling, by the computation device, the accumulation unit to perform an accumulation operation of the convolution operation.

In some possible examples, each secondary operation module includes a second operation unit, a second data dependency determination unit, a second storage unit, and a third storage unit. The above method further includes:

controlling, by the computation device, the second operation unit to perform various arithmetic and logical operations of the secondary operation modules;

controlling, by the computation device, the second data dependency determination unit to perform a reading/writing operation on the second storage unit and the third storage unit during a computation process and ensure that there is no consistency conflict between the reading and writing operations on the second storage unit and the third storage unit;

controlling, by the computation device, the second storage unit to cache input data and an output scalar obtained from the computation performed by the secondary operation modules; and

controlling, by the computation device, the third storage unit to cache a convolution kernel required by the secondary operation module during a computation process.

In some possible examples, the first data dependency or the second data dependency ensures that there is no consistency conflict in reading and writing in the following manners: storage addresses corresponding to data/instructions stored in the corresponding storage unit do not overlap; or determining whether there is dependency between a control signal that has not been executed and data of a control signal that is being executed, if there is no dependency, the control signal is allowed to be issued immediately, otherwise, the control signal is not allowed to be issued until all control signals on which the control signal is dependent have been executed; where

a process of obtaining the control signal includes that the computation device controls the controller unit to obtain an operation instruction from the register unit and decode the operation instruction into the control signal for controlling behavior of other modules, wherein the other modules include the primary operation module and the plurality of secondary operation modules.

In some possible examples, the computation device controls the plurality of secondary operation modules to compute respective output scalars in parallel by using the same input data and respective convolution kernels.

In some possible examples, an activation function active used by the primary operation module may be any of the following non-linear functions: sigmoid, tanh, relu, softmax, or may be a linear function.

In some possible examples, the interconnection module forms a data channel for continuous or discrete data between the primary operation module and the plurality of secondary operation modules. The interconnection module has any of the following structures: a tree structure, a ring structure, a grid structure, a hierarchical interconnection, and a bus structure.

In a second aspect, an example of the present disclosure provides a computation device which includes a function unit configured to perform the methods of the first aspect.

In a third aspect, an example of the present disclosure provides a computer readable storage medium on which a computer program used for electronic data exchange is stored, where the computer program enables a computer to perform the methods of the first aspect.

In a fourth aspect, an example of the present disclosure further provides a computer program product which includes a non-transitory computer readable storage medium storing a computer program. The computer program may cause a computer to perform the methods of the first aspect.

In a fifth aspect, an example of the present disclosure provides a chip which includes the computation device of the second aspect.

In a sixth aspect, an example of the present disclosure provides a chip package structure which includes the chip of the fifth aspect.

In a seventh aspect, an example of the present disclosure provides a board card which includes the chip package structure of the sixth aspect.

In an eighth aspect, an example of the present disclosure provides an electronic device which includes the board card of the seventh aspect.

In some examples, the electronic device includes a data processing device, a robot, a computer, a printer, a scanner, a tablet, a smart terminal, a mobile phone, a traffic recorder, a navigator, a sensor, a webcam, a server, a cloud-based server, a camera, a video camera, a projector, a watch, a headphone, a mobile storage, a wearable device, a vehicle, a household appliance, and/or a medical equipment.

In some examples, the vehicle includes an airplane, a ship, and/or a car. The household electrical appliance includes a television, an air conditioner, a microwave oven, a refrigerator, a rice cooker, a humidifier, a washing machine, an electric lamp, a gas cooker, and a range hood. The medical equipment includes a nuclear magnetic resonance spectrometer, a B-ultrasonic scanner, and/or an electrocardiograph.

Technical effects of implementing the examples of the present disclosure are as follows:

it can be seen that through the examples of the present disclosure, the computation device obtains user data to be processed by controlling an obtaining unit, where the user data is used to recommend product recommendation information of interest to the user; the computation device obtains an operation instruction by controlling an operation unit to call the operation instruction to process the user data, so as to obtain product recommendation information corresponding to the user data, where the operation instruction is a preset instruction for product prediction and recommendation, and the product recommendation information includes at least one target product recommended by the user; in this way, intelligent, quick, and effective product recommendation for users may be realized.

Compared with the prior art using a general-purpose processor for product recommendation, the technical solutions of the present disclosure have technical effects of low power consumption and fast speed.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to illustrate the technical solutions in the examples of the present disclosure more clearly, the drawings to be used in the description of the examples are briefly explained below. Obviously, the drawings in the description below are some examples of the present disclosure. Other drawings can be obtained according to the disclosed drawings without any creative effort by those skilled in the art.

FIG. 1A is a structural diagram of a computation device according to an example of the present disclosure.

FIG. 1B is a schematic flowchart of a convolutional neural network algorithm.

FIG. 1C is a schematic diagram of an instruction of a device supporting a convolutional neural network forward operation according to an example of the present disclosure.

FIG. 1D is a block diagram of an overall structure of a device for performing a convolutional neural network forward operation according to an example of the present disclosure.

FIG. 1E is a structural diagram of an H-tree module (an implementation of an interconnection module) of a device for performing a convolutional neural network forward operation according to an example of the present disclosure.

FIG. 1F is a block diagram of a structure of a primary operation module of a device for performing a convolutional neural network forward operation according to an example of the present disclosure.

FIG. 1G is a block diagram of a structure of a secondary operation module of a device for performing a convolutional neural network forward operation according to an example of the present disclosure.

FIG. 1H is a block diagram of a process of a single-layer convolutional neural network forward operation according to an example of the present disclosure.

FIG. 2 is a flowchart of an information processing method according to an example of the present disclosure.

FIG. 3 is a structural diagram of a network model according to an example of the present disclosure.

FIG. 4 is a schematic diagram of a type of sparse user data according to an example of the present disclosure.

FIG. 5 is a structural diagram of another network model according to an example of the present disclosure.

FIG. 6 is a structural diagram of another computation device according to an example of the present disclosure.

DETAILED DESCRIPTION OF THE EXAMPLES

Technical solutions in examples of the present disclosure will be described clearly and completely hereinafter with reference to the accompanied drawings in the examples of the present disclosure. Obviously, the examples to be described are merely some rather than all examples of the present disclosure. All other examples obtained by those of ordinary skill in the art based on the examples of the present disclosure without creative efforts shall fall within the protection scope of the present disclosure.

Terms such as “first”, “second”, “third”, and “fourth” in the specification, the claims, and the drawings are used for distinguishing different objects rather than describing a specific order. In addition, terms such as “include”, “have”, and any variant thereof are used for indicating non-exclusive inclusion. For instance, a process, a method, a system, a product, or an equipment including a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to the process, the method, the product, or the equipment.

Reference to “example” means that a particular feature, a structure, or a characteristic described in conjunction with the example may be included in at least one example of the present disclosure. The term used in various places in the specification does not necessarily refer to the same example, nor does it refer to an example that is mutually exclusive, independent, or alternative to other examples. It can be explicitly and implicitly understood by those skilled in the art that the examples described herein may be combined with other examples.

First, a computation device used in the present disclosure is introduced. FIG. 1A provides a computation device, where the device includes a storage medium 611 (optional), a register unit 612, an interconnection module 613, an operation unit 614, a control unit 615, and a data access unit 616; where

the operation unit 614 include at least two of the following: an addition arithmetic unit, a multiplication arithmetic unit, a comparator, and an activation arithmetic unit.

The interconnection module 613 is configured to control a connection relationship of the arithmetic units in the operation unit 614 so that the at least two arithmetic units form a different computation topology.

The instruction storage unit (which may be a register unit, an instruction cache, or a scratchpad memory) 612 is configured to store the operation instruction, an address of a data block in the storage medium, and a computation topology corresponding to the operation instruction.

The operation instruction may include an operation field and an opcode. Taking a convolution operation instruction as an example, as shown in Table 1, register 0, register 1, register 2, register 3, and register 4 may be operation fields. Each of register 0, register 1, register 2, register 3, and register 4 may be one or a plurality of registers.

Register Register Register Register Register Opcode 0 1 2 3 4 COM- Input data Input Convolu- Convolu- Address of PUTE starting data tion tion an activation address length kernel kernel function starting length interpolation address table IO Address of Data Address of an external length an internal memory of memory of data data NOP JUMP Target address MOVE Input Data Output address size address

The storage medium 611 may be an off-chip memory, and in certain applications, may also be an on-chip memory for storing a data block. The data block may be n-dimensional data, where n is an integer greater than or equal to 1. For instance, when n=1, the data is one-dimensional data, which is a vector; when n=2, the data is two-dimensional data, which is a matrix; and when n is equal to or greater than 3, the data is multi-dimensional data.

The control unit 615 is configured to fetch an operation instruction, an operation field corresponding to the operation instruction, and a first computation topology corresponding to the operation instruction from the register unit 612, and decode the operation instruction into an execution instruction. The execution instruction is configured to control the operation unit to perform an operation, transfer the operation field to the data access unit 616, and transfer the computation topology to the interconnection module 613.

The data access unit 616 is configured to fetch a data block corresponding to the operation field from the storage medium 611 and transfer the data block to the interconnection module 613.

The interconnection module 613 is configured to receive the first computation topology and the data block. In an example, the interconnection module 613 is further configured to rearrange the data block according to the first computation topology.

The operation unit 614 is configured to call an arithmetic unit of the operation unit 614 according to the execution instruction to perform an operation on the data block to obtain an operation result, transfer the operation result to the data access unit, and store the result in the storage medium. In an example, the operation unit 614 is configured to call an arithmetic unit according to the first computation topology and the execution instruction to perform an operation on the rearranged data block to obtain an operation result, transfer the operation result to the data access unit, and store the result in the memory.

In another example, the interconnection module 613 is configured to form the first computation topology according to the connection relationships of the arithmetic units in the control operation unit 614.

An interconnection module is set in the computation device provided by the present disclosure. The interconnecting module can connect the arithmetic units in the computation unit to obtain a computation topology corresponding to the computation instruction according to the needs of the computation instruction, so that there is no need to store or fetch intermediate data of the computation in subsequent operations of the operation unit. Through this structure, a single instruction can implement a single input and perform operations of a plurality of arithmetic units to obtain a computation result, which improves the computation efficiency.

A computation method of the computation device shown in FIG. 1A is explained below based on different operation instructions. As an instance, the operation instruction may be a convolution operation instruction. The convolution operation instruction can be applied to a neural network, so the convolution operation instruction may also be called a convolutional neural network operation instruction. A formula to be perform by the convolution operation instruction may be: s=s(Σwx_(i)+b), which is to multiply a convolution kernel

by input data x_(i), find the sum, add a bias b, and then perform an activation operation s(h) to obtain a final output result s. According to the formula, the computation topology may be obtained, which is: the multiplication arithmetic unit—the addition arithmetic unit—the (optional) activation arithmetic unit.

A method of performing a convolution operation instruction by the computation device shown in FIG. 1A may include:

fetching, by the control unit 615, a convolution operation instruction, an operation field corresponding to the convolution operation instruction, and the first computation topology (the multiplication arithmetic unit—the addition arithmetic unit—the addition arithmetic unit—the activation arithmetic unit) corresponding to the convolution operation instruction from the register unit 612; transferring, by the control unit, the operation field to a data access unit, and transferring the first computation topology to the interconnection module;

fetching, by the data access unit, a convolution kernel w and a bias b (if b is 0, there is no need to fetch the bias b) corresponding to the operation field from the storage medium, and transferring the convolution kernel w and the bias b to the operation unit; and

multiplying, by the multiplication arithmetic unit of the computation unit, a convolution kernel w and input data Xi to obtain a first result, inputting the first result to the addition arithmetic unit to perform addition to obtain a second result, adding the second result and a bias b to obtain a third result, inputting the third result to the activation arithmetic unit to perform an activation operation to obtain an output result s, transferring the output result s to the data access unit, and storing, by the data access unit, the output result in the storage medium. After each step, the result may be transferred to the data access and stored in storage medium without performing a following step. The step of adding the second result and the bias b to obtain the third result is optional, which means this step is not required when b is 0.

In addition, the order of addition and multiplication can be reversed.

The technical solution provided by the present disclosure can realize convolution operations according to one instruction which is a convolution operation instruction. There is no need to store or obtain intermediate data of convolution operations (such as a first result, a second result, and a third result). The technical solution may reduce the storing and obtaining operations of intermediate data, and may have technical effects of reducing a corresponding operation step and improving outcomes of convolution operations.

It should be understood that the instruction set used in the present disclosure may include one or a plurality of operation instructions. The operation instruction includes, but is not limited to a COMPUTE instruction (an operation instruction), a CONFIG instruction, an IO instruction, an NOP instruction, a JUMP instruction, a MOVE instruction, etc. The COMPUTE instruction includes, but is not limited to, a convolution (CONV) instruction, a pooling operation instruction, etc. Specifically, an executable computation instruction in the present disclosure includes:

a convolution operation instruction. In an example, the convolution COMPUTE instruction (the CONV instruction) includes:

a convolutional neural network sigmoid instruction: according to the instruction, a device fetches input data and a convolution kernel of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs a convolution operation in a convolution operation component, and optionally, performs sigmoid activation on an output result;

a convolutional neural network TanH instruction: according to the instruction, the device fetches input data and a convolution kernel of a specified size from a specified address in a memory (optionally a scratchpad memory) respectively, performs a convolution operation in the convolution operation component, and then performs TanH activation on an output result;

a convolutional neural network ReLU instruction: according to the instruction, the device fetches input data and a convolution kernel of a specified size from a specified address in the memory (optionally a scratchpad memory) respectively, performs a convolution operation in a convolution operation component, and then performs ReLU activation on an output result; and

a convolutional neural network group instruction: according to the instruction, the device fetches input data and a convolution kernel of a specified size from a specified address in the memory (optionally a scratchpad memory) respectively, partitions the input data and the convolution kernel into groups, performs a convolution operation in a convolution operation component, and then performs activation on an output result.

A convolution operation instruction (pure convolution operation instruction): according to the instruction, the device fetches input data and a convolution kernel of a specified size from a specified address in the memory (optionally a scratchpad memory) respectively, and performs a convolution operation in a convolution operation component. The above-mentioned specified size may be set by the user or manufacturer. For instance, in a computation device of a first manufacturer, the specified size may be set to data of A bit, and in a computation device of a second manufacturer, the specified size may be set to data of B bit. The data of A bit and the data of B bit have different sizes.

The pooling instruction. In an example, the pooling COMPUTE instruction (the pooling operation instruction, which is also referred to as the pooling instruction in the present disclosure) specifically includes:

a Maxpooling forward operation instruction: according to the instruction, the device fetches input data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs a Maxpooling forward operation in a pooling operation component, and writes a computation result back to a specified address in the memory (optionally a scratchpad memory or a scalar register file);

a Maxpooling backward training instruction: according to the instruction, the device fetches input data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs Maxpooling backward training in a pooling operation component, and writes a computation result back to a specified address in the memory (optionally a scratchpad memory or a scalar register file);

an Avgpooling forward operation instruction: according to the instruction, the device fetches input data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs an Avgpooling forward operation in a pooling operation component, and writes a computation result back to a specified address in the memory (optionally a scratchpad memory or a scalar register file);

an Avgpooling backward training instruction: according to the instruction, the device fetches input data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs Avgpooling backward training in a pooling operation component, and writes a computation result back to a specified address in the memory (optionally a scratchpad memory or a scalar register file);

a Minpooling forward operation instruction: according to the instruction, the device fetches input data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs a Minpooling forward operation in a pooling operation component, and writes a computation result back to a specified address in the memory (optionally a scratchpad memory or a scalar register file); and a Minpooling backward training instruction: according to the instruction, the device fetches input data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs Minpooling backward training in a pooling operation component, and writes a computation result back to a specified address in the memory (optionally a scratchpad memory or a scalar register file).

A batch normalization instruction can be used for a batch normalization computation.

A fully connected instruction may include a fully connected layer forward operation instruction.

A fully connected layer forward operation instruction: according to the instruction, a device fetches weight data and bias data from a specified address in a memory, performs a full connection operation in a computation unit, and writes a computation result back to a specified address in a scratchpad memory.

The CONFIG instruction configures various constants required by a computation of a current artificial neural network layer before the computation starts. For instance, 1/kernel area can be obtained by configuration using the CONFIG instruction. In the batch normalization computation, the CONFIG instruction configures various constants required for a current layer before a batch normalization computation begins.

The IO instruction is for reading-in input data required for a computation from an external storage space, and storing data to the external space after the computation finishes.

The NOP instruction is for emptying control signals in all control signal cache queues in the current device, and ensuring that all instructions before the NOP instruction are finished. The NOP instruction itself does not include any operations.

The JUMP instruction is for controlling jumping of a next instruction address to be read from an instruction storage unit, so that the jumping of a control flow can be realized.

The MOVE instruction is for moving data of an address in an internal address space of the device to another address in the internal address space of the device. This process is independent of an operation unit and does not occupy resources of the operation unit during execution.

Optionally, in addition to the above instructions, operation instructions that can be executed by the computation device may further include:

a Matrix Mult Vector (MMV) instruction: according to the instruction, the device fetches matrix data and vector data of a set length from a specified address in a scratchpad memory, performs a matrix-multiply-vector operation in the operation unit, and writes a computation result back to a specified address in the scratchpad memory; it is worth noting that a vector can be stored in the scratchpad memory as a matrix of a special form (a matrix with only one row of elements);

a Vector Mult Matrix (VMM) instruction: according to the instruction, the device fetches vector data and matrix data of a set length from a specified address in a scratchpad memory, performs a vector-multiply-matrix operation in the operation unit, and writes a computation result back to a specified address in the scratchpad memory; it is worth noting that a vector can be stored in the scratchpad memory as a matrix of a special form (a matrix with only one row of elements);

a Matrix Mult Scalar (VMS) instruction: according from instruction, the device fetches matrix data of a set length from a specified address in a scratchpad memory, fetches matrix data of a specified size from a specified address of a scalar register file, and performs a scalar-multiply-matrix operation in the operation unit, and writes a computation result back to a specified address in the scratchpad memory; it is worth noting that the scalar register file stores not only an address of the matrix but also scalar data;

a Tensor Operation (TENS) instruction: according to the instruction, the device fetches two pieces of matrix data of a set length from two specified addresses in a scratchpad memory, performs a tensor operation on the two pieces of matrix data in the operation unit, and writes a computation result back to a specified address of the scratchpad memory;

a Matrix Add Matrix (MA) instruction: according to the instruction, the device fetches two pieces of matrix data of a set length from two specified addresses in a scratchpad memory, adds the two pieces of matrix data in the operation unit, and writes a computation result back to a specified address in the scratchpad memory;

a Matrix Sub Matrix (MS) instruction: according to the instruction, the device fetches two pieces of matrix data of a set length from two specified addresses in a scratchpad memory, performs a subtraction operation on the two pieces of matrix data in the operation unit, and writes a computation result back to a specified address in the scratchpad memory;

a Matrix Retrieval (MR) instruction: according to the instruction, the device fetches vector data of a set length from a specified address in a scratchpad memory, fetches matrix data of a specified size from a specified address in the scratchpad memory; in the operation unit, the vector is an index vector, and an i^(th) element of an output vector is a number obtained from an i^(th) column of the matrix by using an i^(th) element of the index vector as an index; and the output vector is written back to a specified address in the scratchpad memory;

a Matrix Load (ML) instruction: according to the instruction, the device loads data of a set length from a specified external source address to a specified address in a scratchpad memory;

a Matrix Store (MS) instruction: according to the instruction, the device stores matrix data of a set length from a specified address in a scratchpad memory to an external target address;

a Matrix Move (MMOVE) instruction: according to the instruction, the device moves matrix data of a set length from a specified address in a scratchpad memory to another specified address in the scratchpad memory;

a Vector-Inner-Product instruction (VP): according to the instruction, the device fetches vector data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs an inner product (a scalar) on two vectors in a vector computation unit, and writes the result back; optionally, the result is written back to a specified address in the memory (optionally a scratchpad memory or a scalar register file);

a vector cross product instruction (TENS): according to the instruction, the device fetches vector data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs an inner product (a scalar) on two vectors in a vector computation unit, and writes the result back; optionally, the result is written back to a specified address in the memory (optionally a scratchpad memory or a scalar register file);

a vector elementary arithmetic operation including a Vector-Add-Scalar instruction (VAS): according to the instruction, the device fetches vector data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), fetches scalar data from a specified address of a scalar register of the memory, adds the scalar to each element of the vector in a scalar computation unit, and writes the result back; optionally, the result is written back to a specified address in the memory (optionally a scratchpad memory or a scalar register file);

a Scalar-Sub-Vector instruction (SSV): according to the instruction, the device fetches scalar data from a specified address in the scalar register in a memory (optionally a scratchpad memory or a scalar register file), fetches vector data from a specified address in the memory (optionally the scratchpad memory or the scalar register file), subtracts corresponding elements of the vector from the scalar in a vector computation unit, and writes the result back; optionally, the result is written back to a specified address in the memory (optionally a scratchpad memory or a scalar register file);

a Vector-Dev-Vector instruction (VD): according to the instruction, the device fetches vector data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs an element-wise division of two vectors in a vector computation unit, and writes the result back; optionally, the result is written back to a specified address in the memory (optionally a scratchpad memory or a scalar register file);

a Scalar-Dev-Vector instruction (SDV): according to the instruction, the device fetches scalar data from a specified address in the scalar register file of a memory (optionally a scratchpad memory or a scalar register file), fetches vector data of a specified size from a specified address in the memory (optionally the scratchpad memory), divides the scalar by corresponding elements in the vector in a vector computation unit, and writes the result back; optionally, the result is written back to a specified position in the memory (optionally a scratchpad memory or a scalar register file).

The computation device can also execute a vector logic instruction, including:

a Vector-AND-Vector instruction (VAV): according to the instruction, the device fetches vector data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file) respectively, performs an element-wise AND on two vectors in a vector computation unit, and writes the result back; optionally, the result is written back to a specified address in the memory (optionally a scratchpad memory or a scalar register file);

a Vector-AND instruction (VAND): according to the instruction, the device fetches vector data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs an element-wise AND operation on two vectors in a vector computation unit, and writes the result back; optionally, the result is written back to a specified address in the scalar register file of the memory (optionally a scratchpad memory or a scalar register file);

a Vector-OR-Vector instruction (VOV): according to the instruction, the device fetches vector data of a specified size from a specified address in a memory (optionally a scratchpad memory) respectively, performs an element-wise OR operation on two vectors in a vector computation unit, and writes the result back; optionally, the result is written back to a specified address in the memory (optionally a scratchpad memory or a scalar register file);

a Vector-OR instruction (VOR): according to the instruction, the device fetches vector data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs an OR operation on each element of the vector in a vector computation unit, and writes the result back; optionally, the result is written back to a specified address in the scalar register file of the memory (optionally a scratchpad memory or a scalar register file);

a transcendental function instruction: according to the instruction, the device fetches vector data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs a transcendental function operation on the vector data in an operation unit, and writes the result back; optionally, the result is written back to a specified address in a storage unit of the memory (optionally a scratchpad memory or a scalar register file). Optionally, the result is written back to a specified address in the memory (optionally a scratchpad memory or a scalar register file);

The computation device can also execute a vector comparison operation instruction, including:

a Greater-Equal operation instruction (GE): according to the instruction, the device may obtain parameters of the instruction, including a length of a vector, a starting address of two vectors, and a storage address of an output vector, directly from the instruction or by accessing the serial number of the register of a memory (optionally a scratchpad memory or a scalar register file) provided by the instruction, then read data of the two vectors, and compare the elements at all positions in the vectors in a vector comparison operation unit; at the position of a row, if the value of a previous vector is greater than or equal to the value of a subsequent vector, the value of the comparison result vector at that position is set to 1, otherwise it is set to 0; finally, the comparison result is written back to a specified storage address in the memory (optionally the scratchpad memory or the scalar register file);

a Less-Equal operation instruction (LE): according to the instruction, the device may obtain the parameters of the instruction, including the length of a vector, the starting address of the two vectors, and the storage address of the output vector, directly from the instruction or by accessing the serial number of the register of a memory (optionally a scratchpad memory or a scalar register file) provided by the instruction, then read the data of the two vectors, and compare the elements at all positions in the vectors in a vector comparison operation unit; at the position of a row, if the value of a previous vector is less than or equal to the value of a subsequent vector, the value of the comparison result vector at that position is set to 1, otherwise it is set to 0; finally, the comparison result is written back to a specified storage address in the memory (optionally the scratchpad memory or the scalar register file);

a Greater-Than operation instruction (GT): according to the instruction, the device may obtain the parameters of the instruction, including the length of a vector, the starting address of the two vectors, and the storage address of the output vector, directly from the instruction or by accessing the serial number of the register of a memory (optionally a scratchpad memory or a scalar register file) provided by the instruction, then read the data of the two vectors, and compare the elements at all positions in the vectors in a vector comparison operation unit; at the position of a row, if the value of a previous vector is greater than the value of a subsequent vector, the value of the comparison result vector at that position is set to 1, otherwise it is set to 0; finally, the comparison result is written back to a specified storage address in the memory (optionally the scratchpad memory or the scalar register file);

a Less-Than operation instruction (LT): according to the instruction, the device may obtain the parameters of the instruction, including the length of a vector, the starting address of the two vectors, and the storage address of the output vector, directly from the instruction or by accessing the serial number of the register of a memory (optionally a scratchpad memory or a scalar register file) provided by the instruction, then read the data of the two vectors, and compare the elements at all positions in the vectors in a vector comparison operation unit; at the position of a row, if the value of a previous vector is less than the value of a subsequent vector, the value of the comparison result vector at that position is set to 1, otherwise it is set to 0; finally, the comparison result is written back to a specified storage address in the memory (optionally the scratchpad memory or the scalar register file);

an Equal operation instruction (EQ): according to the instruction, the device may obtain the parameters of the instruction, including the length of a vector, the starting address of the two vectors, and the storage address of the output vector, directly from the instruction or by accessing the serial number of the register of a memory (optionally a scratchpad memory or a scalar register file) provided by the instruction, then read the data of the two vectors, and compare the elements at all positions in the vectors in a vector comparison operation unit; at the position of a row, if the value of a previous vector is equal to the value of a subsequent vector, the value of the comparison result vector at that position is set to 1, otherwise it is set to 0; finally, the comparison result is written back to a specified storage address in the memory (optionally the scratchpad memory or the scalar register file);

an Unequal operation instruction (UEQ): according to the instruction, the device may obtain the parameters of the instruction, including the length of a vector, the starting address of the two vectors, and the storage address of the output vector, directly from the instruction or by accessing the serial number of the register of a memory (optionally a scratchpad memory or a scalar register file) provided by the instruction, then read the data of the two vectors, and compare the elements at all positions in the vectors in a vector comparison operation unit; at the position of a row, if the value of a previous vector is unequal to the value of a subsequent vector, the value of the comparison result vector at that position is set to 1, otherwise it is set to 0; finally, the comparison result is written back to a specified storage address in the memory (optionally the scratchpad memory or the scalar register file);

a Vector Max instruction (VMAX): according to the instruction, the device fetches vector data of a specified size from a specified address in a scratchpad memory of a memory (optionally a scratchpad memory or a scalar register file), selects a largest element from the vector data as a result, and writes the result back; optionally, the result is written back to a specified address in the scalar register file of the memory (optionally a scratchpad memory or a scalar register file);

a Vector Min instruction (VMIN): according to the instruction, the device fetches vector data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), selects a minimum element from the vector data as a result, and writes the result back; optionally, the result is written back to a specified address in the scalar register file of the memory (optionally a scratchpad memory or a scalar register file);

a Cyclic Shift operation instruction: according to the instruction, the device may obtain parameters of the instruction directly from the instruction or by accessing the serial number of the register of a memory (optionally a scratchpad memory or a scalar register file) provided by the instruction, then cyclically shift vectors in a vector shift unit (which may be a separate vector shift unit or a computation unit), and then write the result of the shift back to a specified storage address in the memory (optionally the scratchpad memory or the scalar register file); where a format of the cyclic shift operation instruction format may include four operation fields, a starting address and length of a vector, a shift stride, and a storage address of an output vector; and

a Random-Vector generation instruction: according to the instruction, the device reads one or more randomly distributed parameters, and the size and storage address of a random vector to be generated from the instruction or from the register of a memory (optionally a scratchpad memory or a scalar register file), generates the random vector that is in line with the random distribution in a random vector generation unit, and then writes the result of the random vector back to the specified storage address in the memory (optionally the scratchpad memory or the scalar register file).

The Random-Vector generation instruction may be:

a Uniform distribution instruction (UNIF): according to the instruction, the device reads uniformly distributed upper and lower bound parameters, and the size and storage address of the random vector to be generated from the instruction or from the register of a memory (optionally a scratchpad memory or a scalar register file), generates the random vector that is in line with the uniform distribution in a random vector generation unit, and then writes the result of the random vector back to the specified storage address in the memory (optionally the scratchpad memory or the scalar register file); and

a Gaussian distribution instruction (GAUS): according to the instruction, the device reads Gaussian distributed mean and variance parameters, and the size and storage address of the random vector to be generated from the instruction or from the register of a memory (optionally a scratchpad memory or a scalar register file), generates the random vector that is in line with the Gaussian distribution in a random vector generation unit, and then writes the result of the random vector back to the specified storage address in the memory (optionally the scratchpad memory or the scalar register file).

During execution of a convolutional neural network algorithm (a convolution operation instruction) by the computation device shown in FIG. 1A, please refer to the flowchart of the convolutional neural network algorithm shown in FIG. 1B. As shown in FIG. 1B, a convolutional neural network includes output data, an activation function, an input data layer, and a convolution kernel.

Each computation process includes: selecting corresponding input data x_(i) in the input data layer according to a convolution window, and then performing an addition operation on the input data and the convolution kernel. A computation process of the output data is s=s(Σwx_(i)+b) which is to multiply a convolution kernel w by input data x_(i), find the sum, add a bias b, and then perform an activation operation s(h) to obtain a final output data s. The multiplication of the convolution kernel and the input data is vector multiplication.

According to the size k_(x) of the convolution kernel on an X axis and the size k_(y) of the convolution kernel on the Y axis, the convolution window firstly selects input data of which the size is the same as that of the convolution kernel from the input data of which the size of the X axis is W and the size of the Y axis is H, performs horizontal translation and then vertical translation according to translation position vectors S_(x) and S_(y) of the convolution window, and traverses all the input data.

FIG. 1C shows a format of an instruction set according to an example of the present disclosure. As shown in the figure, a convolutional neural network operation instruction includes at least one opcode and at least one operation field. The opcode is for indicating a function of the convolutional neural network operation instruction. A convolutional neural network operation unit can perform a convolutional neural network operation by identifying the opcode. The operation field is for indicating data information of the convolutional neural network operation instruction. The data information may be an immediate operand or a register number (which, optionally, may be a register file), which includes a starting address and a length of input data, a starting address and a length of the convolution kernel, and a type of an activation function.

The instruction set includes: convolutional neural network COMPUTE instruction with different functions, a CONFIG instruction, an IO instruction, an NOP instruction, a JUMP instruction, and a MOVE instruction. The above operation instructions will not be further described herein. For details, please refer to related descriptions in the above examples.

Optionally, the instruction set may further include a convolution activation CONV_ACTIVATE instruction.

The convolution activation CONV_ACTIVATE instruction: according to the instruction, the device fetches input data and a convolution kernel of a specified size from a specified address in the scratchpad memory (optionally), performs a convolution operation in a convolution operation component, and then performs an activation function operation on an output result; the above-mentioned specified size may be set by the manufacturer or user.

In one example, the CONV_ACTIVATE instruction includes: a convolution operation instruction and an activation instruction. The activation instruction is configured to perform an activation function operation, and the convolution operation instruction is configured to perform a convolution operation. For details, please refer to related descriptions in the above examples.

FIG. 1D is a schematic structural diagram of a device for performing a convolutional neural network forward operation according to an example of the present disclosure. As shown in FIG. 3, the device includes an instruction storage unit 1, a controller unit 2, a data access unit 3, an interconnection module 4, a primary operation module 5, and a plurality of secondary operation modules 6. The instruction storage unit 1, the controller unit 2, the data access unit 3, the interconnection module 4, the primary operation module 5, and the plurality of secondary operation modules 6 may all be realized in a form of a hardware circuit (for instance, including but not limited to FPGA, CGRA, ASIC, analog circuit, memristor, etc.).

The instruction storage unit 1 is configured to read an instruction through the data access unit 3 and store the instruction.

The controller unit 2 is configured to read an instruction from the instruction storage unit 1, decode the instruction into a control signal for controlling the behavior of other modules, and send the instruction to other modules such as the data access unit 3, the primary operation module 5, and the plurality of secondary operation modules 6.

The data access unit 3 can access an external address space, directly read and write data to each storage unit inside the device to complete the loading and storage of the data,

The interconnection module 4 is configured to connect the primary operation module and the secondary operation modules, and can be implemented into different interconnection topologies (such as tree structure, ring structure, grid structure, hierarchical interconnection, bus structure, etc.).

FIG. 1E schematically shows an implementation of the interconnection module 4: an H-tree module. The interconnection module 4 forms a data path between the primary operation module 5 and the plurality of secondary operation modules 6, where the data path is a binary tree path composed of a plurality of nodes. Each node can transfer data received from an upstream node to two downstream nodes, and merge data returned by the two downstream nodes and return to an upstream node. For instance, at the beginning of a computational phase of each layer of an artificial neural network, neuron data in the primary operation module 5 is sent to each secondary operation module 6 through the interconnection module 4; when the secondary operation modules 6 finish computing, neuron values output by the respective secondary operation modules are spliced stage-by-stage into a complete vector composed of neurons in the interconnection module. For instance, if there are N secondary operation modules in the device, input data x_(i) is transferred to the N secondary operation modules and each of the secondary operation modules performs a convolution operation on the input data x_(i) and the convolution kernel corresponding to the secondary operation module to obtain scalar data. The scalar data of each secondary operation module are merged into an intermediate vector including N elements by the interconnection module 4. If the convolution window obtains a total of A*B pieces of (A pieces in the X direction, B pieces in the Y direction, where X and Y are coordinate axes of the three-dimensional orthogonal coordinate system) input data x_(i) by traverse, a convolution operation is perform on the above A*B pieces of x_(i) and all the vectors obtained are merged in the primary operation module to obtain a three-dimensional intermediate result of A*B*N.

FIG. 1F is a block diagram of a structure of the primary operation module 5 of a device for performing a convolutional neural network forward operation according to an example of the present disclosure. As shown in FIG. 1F, the primary operation module 5 includes a first operation unit 51, a first data dependency determination unit 52, and a first storage unit 53.

The first operation unit 51 includes a vector addition unit 511 and an activation unit 512. The first operation unit 51 is configured to receive a control signal from the controller unit and complete various operational functions of the primary operation module 5. The vector addition unit 511 is configured to perform an operation of adding a bias in the forward computation of the convolutional neural network, and perform element-wise addition on bias data and the intermediate results to obtain a bias result. The activation operation unit 512 performs an activation function operation on the bias result. The bias data may be read in from an external address space, or may be stored locally.

The data dependency determination unit 52 is a port for the first operation unit 51 to read/write the first storage unit 53, so as to ensure consistency in reading data from and writing data to the first storage unit 53. At the same time, the first data dependency determination unit 52 is also configured to send data read from the first storage unit 53 to the secondary operation modules through the interconnection module 4. Output data of the secondary operation modules 6 is directly sent to the first operation unit 51 through the interconnection module 4. An instruction output by the controller unit 2 is sent to the operation unit 51 and the first data dependency determination unit 52 to control their behavior.

The storage unit 53 is configured to cache input data and output data used by the primary operation module 5 during a computation process.

FIG. 1G is a block diagram of a structure of the secondary operation modules 6 of a device for performing a convolutional neural network forward operation according to an example of the present disclosure. As shown in FIG. 1E, each secondary operation module 6 includes a second operation unit 61, a data dependency determination unit 62, a second storage unit 63, and a third storage unit 64.

The second operation unit 61 is configured to receive a control signal from the controller unit 2 and perform a convolution operation. The second operation unit includes a vector multiplication unit 611 and an accumulation unit 612, which are respectively responsible for a vector multiplication operation and an accumulation operation in a convolution operation.

The second data dependency determination unit 62 is responsible for reading and writing the second storage unit 63 during a computation process. Before performing read/write operations, the second data dependency determination unit 62 first ensures that there is no consistency conflict between the reading and writing of data used by instructions. For instance, all control signals sent to the data dependency unit 62 are stored in the instruction queue inside the data dependency unit 62. In this queue, if a range of data to be read by a reading instruction conflicts with a range of data to be written by a writing instruction that is located at the front of the queue, the instruction can only be executed until a writing instruction depended by the instruction has been executed.

The second storage unit 63 is configured to cache input data and output scalar data of the secondary operation modules 6.

The third storage unit 64 is configured to cache convolution kernel data required by the secondary operation modules 6 in a computation process.

FIG. 1H is a flowchart of executing a convolutional neural network by a convolutional neural network operation device according to an example of the present disclosure. As shown in FIG. 1H, a process of executing the convolutional neural network neural network instruction includes:

a step S1, pre-storing an IO instruction in a starting address of the instruction storage unit 1;

a step S2, the operation starts, reading, by the controller unit 2, the IO instruction from the starting address of the instruction storage unit 1, and according to a control signal decoded from the instruction, reading, by the data access unit 3, all corresponding convolutional neural network operation instructions from an external address space, and caching the instructions in the instruction storage unit 1;

a step S3, reading, by the controller unit 2, a next IO instruction from the instruction storage unit, and according to a control signal obtained by decoding, reading, by the data access unit 3, all data (such as input data, an interpolation table for a quick activation function operation, a constant table for configuring parameters of the operation device, bias data, etc.) required by the primary operation module 5 from the external address space to the first storage unit 53 of the primary operation module 5;

a step S4, reading, by the controller unit 2, a next IO instruction from the instruction storage unit, and according to a control signal decoded from the instruction, reading, by the data access unit 3, convolution kernel data required by the secondary operation modules 6 from the external address space;

a step S5, reading, by the controller unit 2, a next CONFIG instruction from the instruction storage unit, and according to a control signal obtained by decoding, configuring, by the device, various constants required by the computation of the neural network layer; for instance, the first operation unit 51 and the second operation unit 61 may configure a value of an internal register of the parameter configuration unit in the control signal, where the parameter includes, for instance, data required by an activation function;

a step S6, reading, by the controller unit 2, a next COMPUTE instruction from the instruction storage unit, and according to a control signal decoded from the instruction, sending, by the primary operation module 5, input data in a convolution window to each secondary operation module 6 through an interconnection module 4 and saving the input data to the second storage unit 63 of the secondary operation module 6; and then moving the convolution window according to the instruction;

a step S7, according to the control signal decoded from the COMPUTE instruction, reading, by the operation unit 61 of the secondary operation module 6, the convolution kernel from the third storage unit 64; reading the input data from the second storage unit 63 to complete the convolution operation of the input data and the convolution kernel; and returning an obtained intermediate result through the interconnection module 4;

a step S8, in the interconnection module 4, splicing intermediate results returned from respective secondary operation modules 6 stage by stage to obtain a complete intermediate vector;

a step S9, obtaining, by the primary operation module 5, the intermediate vector returned by the interconnection module 4; traversing, by the convolution window, all input data; splicing, by the primary operation module, all returned vectors into an intermediate result; according to the control signal decoded from the COMPUTE instruction, reading bias data from the first storage unit 53, adding the intermediate result and the bias data in a vector addition unit 511 to obtain a bias result; activating the bias result by the activation unit 512, and writing final output data back to the first storage unit; and

a step S10, reading, by the controller unit 2, a next TO instruction from the instruction storage unit, and according to a control signal decoded from the instruction, storing, by the data access unit 3, the output data in the first storage unit 53 to a specified address in the external address space, then the operation finishes.

The implementation of a multi-layer convolutional neural network is similar to that of a single-layer convolutional neural network. After an upper layer of the convolutional neural network is executed, an operation instruction of a next layer uses an output data address of the upper layer stored in the primary operation unit as an input data address of this layer. Similarly, the address of a convolution kernel and the address of bias data in the instruction may also be changed to an address corresponding to this layer.

The present disclosure uses a device and an instruction set for performing the convolutional neural network forward operation, which solves the problem of the lack of CPU and GPU computation performance, and the problem of high front-end decoding overhead. The present disclosure effectively improves support for the forward operation of a multi-layer convolutional neural network.

By using a dedicated on-chip cache for the forward operation of a multi-layer convolutional neural network, input neurons and convolution kernel data may be fully reused, which may avoid repeated reading of these data from the memory, reduce the memory access bandwidth, and prevent the memory bandwidth from becoming a performance bottleneck of the forward operation of a multi-layer artificial neural network.

Based on the above examples, FIG. 2 shows an information processing method according to an example of the present disclosure. The method shown in FIG. 2 may include:

a step S102, obtaining, by the computation device, user data to be processed, where the user data is used to recommend product recommendation information of interest to the user.

The user data is used to recommend product recommendation information of interest to the user. The user data may be a set of user data of a certain user, or may be multiple sets of user data corresponding to multiple users, which is not limited herein. The user data may include, but is not limited to, one or more of the following: user behavior data, user personal data, user physical sign data, group data, and time data. The user behavior data is used to characterize/indicate information of historical product records of the user, such as historical purchase records and historical browsing records for products. The physical sign data of the user is used to indicate physiological data of the user, such as height, weight, and breathing rate. The user personal data may include, but is not limited to, the user's age, height, location (geographical location), education level, etc. The group data is used to indicate user data of other users within a preset distance range of the user (within a same geographic area as the user, such as in a same community or city). The time data is used to indicate occurrence time of the user behavior data, such as time of a historical purchase record for a product.

The method further includes a step S104, using, by the computation device, the user data as an input of the operation unit and call an operation instruction to process the user data, so as to obtain product recommendation information corresponding to the user data, where

the operation instruction is a preset instruction for product prediction and recommendation, and the product recommendation information includes at least one target product recommended for the user.

The operation instruction includes, but is not limited to, a convolution operation instruction, a pooling instruction, a fully connected instruction, a batch normalization instruction, an activation softmax instruction, a normalization instruction, a non-linear activation instruction, and the like. For details, please refer to related descriptions in the above examples. Optionally, the process of calling related operation instructions in the computation device (such as an operation unit) to process the user data will not be further described herein. For details, please refer to the specific descriptions of calling related instruction in the above examples.

Some examples involved in the present disclosure are described below.

In the step S102, the computation device obtains user data to be processed through the communication unit. In an optional example, the communication unit may be the storage medium (the off-chip memory) shown in 1A or be an input/output (IO) unit, which is not limited herein.

In an optional example, the computation device may be the computation device shown in FIG. 1A or FIG. 1D. Specifically, the computation device can store various operation instructions in the register unit or the instruction storage unit through the data access unit; further, the computation device can read/write and store various operation instructions through the data access unit. The controller unit is configured to control the reading of various operation instructions from the register unit (or the instruction storage unit, etc.) and decode the operation instruction into an executable operation instruction. Optionally, the controller unit may also send the operation instruction to the operation unit for execution. Specifically, related arithmetic units can be called in turn for data processing according to the computation topology corresponding to the operation instruction. The convolution operation instruction is described in details below as an instance. The interconnection module is configured to receive input data (the user data) and a computation topology, where the computation topology is a topology corresponding to the operation instruction. For instance, when the operation instruction is a convolution operation instruction, the corresponding computation topology may be: the multiplication arithmetic unit—the addition arithmetic unit—(optional) the activation arithmetic unit. Each type of arithmetic unit is configured to perform a corresponding operation function operation, for instance, the multiplication arithmetic unit is configured to perform a multiplication operation, etc., which will not be further described in the present disclosure.

Some examples involved in the step S104 are described below.

In a specific implementation, the computation device may input the user data into the network model to call related operation instructions to perform feature extraction on the user data, so as to obtain a score corresponding to each product in a database. Further, the computation device may select a product whose score meets a preset condition from a plurality of products as a target product recommended for the user. Further, the product recommendation information is output and includes each target product determined above.

It should be understood that the process of performing feature extraction on the user data is also a process of data dimensionality reduction, which is to reduce high-dimensional user data to low-dimensional data, thereby reducing an amount of operation data of the computation device, and improving computation speed and efficiency.

In an optional example, the network model includes, but is not limited to, a neural network model and a non-neural network model. The neural network model includes, but is not limited to, a deep neural network (DNN) model, a convolutional neural network (CNN) model, a recurrent neural network (RNN) model, a back-propagation (BP) neural network model, a long and short term memory (LSTM) network model, a gate repeat unit (GRU) model, or other neural network models, which is not limited in the present disclosure. Optionally, the neural network model may be composed of any one or more of the following functional layers: a convolution operation layer, a pooling layer, an activation softmax layer, a batch normalization layer, and a fully connected layer, where an operation of each function layer is implemented by at least one pre-stored operation instruction. In addition, a corresponding operation instruction may be designed for each functional layer in the present disclosure, so as to implement the operation in the functional layer. For instance, a fully connected instruction is designed for a fully connected layer, so as to call the fully connected layer to implement the operation of the fully connected layer; and the operation of the convolution operation layer can be implemented by the convolution operation instruction, etc.

In an optional example, the functional layers composing the neural network model, a count of the functional layers, and an order of the functional layers can be customized by a user side or a terminal side, which are not limited in the present disclosure.

FIG. 3 is a structural diagram of a network model. It can be seen from the figure that the user data may include the user behavior data, user personal data, user physical sign data, group data, and time data, etc., may include terminal data, for instance, whether the device inputting the user data is a mobile phone or a computer, etc., may also include application program information included in the computation device (terminal), which is not limited herein. Optionally, the computation device may perform concatenated embedding processing on input user data and product data (product data of a plurality of products) pre-stored in a database. In this process, any one or more of the following functional layers in the following neural network models may be used: a fully connected layer (MLP), a convolutional neural network (CONV), a restricted Boltzmann machine (RBM), and other neural network layers.

It should be understood that the neural network layer or function layer may be composed of any one or more of the following operation instructions: a fully connected instruction, a convolution operation instruction, a matrix multiplication instruction, and the like.

As shown in FIG. 3, a matrix operation module may be configured to debug model parameters of the network model design and determine the product recommendation information during the network model training process. Specifically, the computation device may perform a specified matrix operation, such as matrix addition, matrix multiplication, etc., on input data (the user data) and output data processed by the network model (such as a vector containing respective scores of a plurality of products) to determine a target product recommended for the user (in other words, product recommendation information).

In practical applications, if there are situations where some user data is not collected completely, the user data may be regarded as sparse user data. For sparse user data, the computation device uses a same method as described above. The method includes performing feature extraction on the sparse user data by using an operation instruction associated with a neural network model to reduce high-dimensional data to low-dimensional data, so as to complete product recommendation for the user.

FIG. 4 is a schematic diagram of a type of sparse user data. FIG. 4 shows rating data of other users within respective geographic areas of users A, B, and C for different movies. The data “0” indicates that a rating of the user for a movie is missing. Letters such as TI, NM, SW and ST are used to indicate classifications of movies. X⁽¹⁾ to X⁽⁷⁾ respectively represent ratings of other seven users within respective area of the users A, B and C for different movies. A feature vector x represents input user data. A target Y represents output product recommendation information (a target product).

FIG. 5 is a structural diagram of a neural network model for processing the sparse user data. As shown in FIG. 5, the neural network model may be composed of a fully connected layer and an activation layer (CTR).

Correspondingly, after operations of the activation layer and the softmax layer, a score corresponding to each product in a database may be generated, then the scores may be sorted using a sorting algorithm, and top n products are selected as the target products (product recommendation information) to be recommended to the user, where n is a positive integer.

In an optional example, the computation device may display the product recommendation information on a terminal display interface in real time or periodically for the user to view.

In an optional example, a specific implementation of the step S104 is briefly described below combined with the above examples.

In a specific implementation, the computation device fetches a corresponding operation instruction from the register unit (or the instruction storage unit) through the controller unit and the data access unit, where the operation instruction is configured to process the first language information. For the operation instruction, please refer to the related introduction in the above examples; for instance, the instruction may be the operation instruction associated with a network model. The count of the operation instructions is not limited herein.

Further, after the controller unit fetches the operation instruction, the controller unit sends the operation instruction to the operation unit to process the user data in the operation unit according to the computation topology corresponding to the operation instruction, so as to obtain the product recommendation information.

A specific implementation process of the step S104 is described in detail below with the operation instruction being a convolution operation instruction as an instance.

In a specific implementation, referring to the computation device shown in FIG. 1A, the computation device obtains user data x_(i) to be processed through the communication unit (or a storage medium, or an off-chip memory). Further, the computation device fetches a convolution operation instruction from the register unit through the data access unit and the controller unit, and sends the convolution operation instruction to the operation unit for execution, in other words, a formula to be executed is s=s(Σwx_(i)+b). w is convolution kernel, and x_(i) is input data. Correspondingly, the computation device controls the operation unit to execute the convolution operation instruction on the input data x_(i) (the user data). Specifically, the computation device calls the multiplication arithmetic unit in the operation unit to multiply a convolution kernel w by input data x_(i), calls the addition arithmetic unit to find the sum, adds a bias b, and then calls the activation arithmetic unit to perform an activation operation s(h), so as to obtain a final output result s. The output result may be the commodity recommendation information or respective recommendation information or intermediate data for a plurality of products. When the output result is intermediate data, according to a similar computation principle of the above convolution operation instruction, the computation device may further call other operation instructions to process the intermediate data. The process is repeated until the product recommendation information is obtained.

In another specific implementation, referring to the computation device shown in FIG. 1D, the process is similar to that of the above step S104 and uses the computation device shown in 1D. The operation unit may specifically include a primary operation module, secondary operation modules, and an interconnection module connecting the primary operation module and the secondary operation modules. The interconnection module may be configured to transfer data between the primary operation module and the secondary operation modules, receive a computation topology corresponding to an operation instruction, etc. The computation device may control a implementation of a bias b operation and an activation S (h) operation in the convolution operation in the primary operation module, and control a implementation of a vector multiplication operation wx_(i) and an accumulation operation Σ in the respective secondary operation modules. Specifically, the computation device may transfer input data x_(i) (the first language information) to each secondary operation module through the controller unit, so as to first call a multiplication arithmetic unit to multiply a convolution kernel

by the input data x_(i), and then call an addition arithmetic unit to sum and obtain an output scalar. Then the interconnection module is configured to accumulate and splice output scalars of the respective secondary operation modules stage by stage into an intermediate vector and send the intermediate vector to the primary operation module. Further, the computation device calls the addition arithmetic unit in the primary operation module to splice intermediate vectors corresponding to all input data into an intermediate result, adds a bias b to the intermediate result, and then calls an activation arithmetic unit to perform an activation operation s(h) to obtain a final output result s.

For the implementation of calling related operation instructions in the computation device to process the first language information, please refer to related descriptions of the above FIGS. 1A to 1H. In other words, the examples of the FIGS. 1A to 1H may also be correspondingly applied to the examples of the information processing method described in FIG. 2, and will not be further described herein. It should be understood that the convolution operation instruction in the above description is only used as an instance to illustrate the call and data processing of the convolution operation instruction, which is not a limitation; accordingly, when the operation instruction is another instruction instead of the convolution operation instruction, a related processing method similar to that of the convolution operation instruction may also be used to implement steps of the method examples of the present disclosure.

FIG. 6 is a structural diagram of a computation device (which may specifically be a terminal device) according to an example of the present disclosure. The computation device shown in FIG. 6 includes a communication unit 617 and an operation unit 614, where

the communication unit 617 is configured to obtain user data to be processed, where the user data is used to recommend product recommendation information of interest to the user;

the operation unit is configured to obtain and call an operation instruction to process the user data to obtain product recommendation information corresponding to the user data; where

the operation instruction is a preset instruction for product prediction and recommendation, and the product recommendation information includes at least one target product recommended for the user.

Optionally, the computation device further includes a storage medium 611 (optional), a register unit 612, an interconnection module 614, a controller 615, and a data access unit 616. For the above function units, please refer to related descriptions of the above examples. Optionally, the communication unit and the storage medium may be the same or different. For instance, the communication unit may be a storage medium or be an (10) unit of the computation device, which is not limited herein.

In an optional example, the user data includes at least one of the following: user behavior data, user physical sign data, group data, and time data, where the user behavior data is used to indicate historical product records of the user, the user's physical sign data is used to indicate physiological data of the user, the group data is used to indicate data of other users within a same geographic area as the user, and the time data is used to indicate time of generation of the user behavior data.

In an optional example, the computation device further includes a register unit 612 and a controller unit 615, where

the register unit is configured to store various operation instructions and computation topologies corresponding to the operation instructions;

the controller unit is configured to fetch an operation instruction associated with a network model from the register unit, decode the operation instruction into an executable operation instruction, and send the operation instruction and a computation topology corresponding to the operation instruction to the operation unit; and

the operation unit is configured to call an executable operation instruction associated with the network model to perform feature extraction on the user data to obtain respective recommendation information of a plurality of products, where the recommendation information at least includes a score of the product; and

the operation unit is configured to select recommendation information whose score exceeds a preset threshold from the plurality of recommendation information as the product recommendation information.

In an optional example, the network model includes any one or more of the following functional layers: a convolution operation layer, a pooling layer, an activation softmax layer, a batch normalization layer, and a fully connected layer; where the function layers are composed of at least one pre-stored operation instruction.

In an optional example, the computation device further includes a data access unit 616 and a storage medium 611,

the operation unit is further configured to send the product recommendation information to the data access unit and store the product recommendation information in the storage medium.

In an optional example, the operation unit includes a primary operation module and a plurality of secondary operation modules, where the primary operation module is interconnected with the plurality of secondary operation modules by an interconnection module, and when the operation instruction is a convolution operation instruction,

the secondary operation modules are configured to implement a convolution operation of input data and convolution kernels in a convolutional neural network algorithm, wherein the input data is the user data and the convolutional neural network algorithm corresponds to the convolution operation instruction,

the interconnection module is configured to implement data transfer between the primary operation module and the secondary operation modules; before a forward operation of a neural network fully connected layer starts, the primary operation module sends the input data to each secondary operation module through the interconnection module; and after the computation of the secondary operation modules is completed, the interconnection module splices output scalars of the respective secondary operation modules stage by stage into an intermediate vector and sends the intermediate vector back to the primary operation module, and

the primary operation module is configured to splice intermediate vectors corresponding to all input data into an intermediate result, and perform subsequent operations on the intermediate result, where

In an optional example,

the primary operation module is configured to add bias data to the intermediate result, and then perform an activation operation.

In an optional example, the primary operation module includes a first operation unit, where the first operation unit includes a vector addition unit and an activation unit,

the vector addition unit is configured to implement a bias addition operation of a convolutional neural network operation and perform element-wise addition on bias data and the intermediate result to obtain a bias result; and

the activation unit is configured to perform an activation function operation on the bias result.

In an optional example, the primary operation module includes a first storage unit, a first operation unit, a first data dependency determination unit, and a first storage unit; where

the first storage unit is configured to cache input data and output data used by the primary operation module during a computation process, where the output data includes respective recommendation information of the plurality of products and/or the product recommendation information;

the first operation unit is configured to perform various operational functions of the primary operation module,

The data dependency determination unit is configured to ensure that there is no consistency conflict in reading data from and writing data to the first storage unit, read an input neuron vector from the first storage unit, and send the vector to the secondary operation modules through the interconnection module; and

sending an intermediate result vector from the interconnection module to the first operation unit.

In an optional example, the secondary operation modules include a second operation unit, where the second operation unit includes a vector multiplication unit and an accumulation unit,

the vector multiplication unit is configured to perform a vector multiplication operation of a convolution operation, and

the accumulation unit is configured to perform an accumulation operation of the convolution operation.

In an optional example, each secondary operation module includes a second operation unit, a second data dependency determination unit, a second storage unit, and a third storage unit;

the second operation unit is configured to perform various arithmetic and logical operations of the secondary operation modules,

the second data dependency determination unit is configured to perform a reading/writing operation on the second storage unit and the third storage unit during a computation process to ensure that there is no consistency conflict between the reading and writing operations on the second storage unit and the third storage unit,

the second storage unit is configured to cache input data and an output scalar obtained from the computation performed by the secondary operation module, and

the third storage unit is configured to cache a convolution kernel required by the secondary operation module in the computation process.

In an optional example, the first data dependency or the second data dependency ensures that there is no consistency conflict in reading and writing in the following manners: storage addresses corresponding to data/instructions stored in the corresponding storage unit do not overlap; or determining whether there is dependency between a control signal that has not been executed and data of a control signal that is being executed; if there is no dependency, the control signal is allowed to be issued immediately; otherwise, the control signal is not allowed to be issued until all control signals on which the control signal is dependent have been executed; where

the computation device controls the controller unit to obtain an operation instruction from the register unit and decode the operation instruction into the control signal for controlling behavior of other modules, wherein the other modules include the primary operation module and the plurality of secondary operation modules.

In an optional example, the plurality of secondary operation modules are configured to compute respective output scalars in parallel by configuration using the same input data and respective convolution kernels.

In an optional example, an activation function active used by the primary operation module may be any of the following non-linear functions: sigmoid, tanh, relu, softmax, or may be a linear function.

In an optional example, the interconnection module forms a data channel for continuous or discrete data between the primary operation module and the plurality of secondary operation modules. The interconnection module has any of the following structures: a tree structure, a ring structure, a grid structure, a hierarchical interconnection, and a bus structure.

For those parts which are not shown or described in the examples of the present disclosure, please refer to related descriptions of the above examples shown in FIGS. 1A to 5.

An example of the present disclosure further provides a computer storage medium on which a computer program is stored for electronic data exchange. The computer program may cause a computer to perform part or all of the steps of any information processing method described in the foregoing method examples.

An example of the present disclosure further provides a computer program product, where the computer program product includes a non-transitory computer-readable storage medium storing a computer program. The computer program may cause a computer to perform part or all of the steps of any information processing method described in the foregoing method examples.

An example of the present disclosure also provides an acceleration device which includes: a memory which stores executable instructions, and a processor configured to execute the executable instructions in the storage unit according to the information processing method.

The processing unit may be a single one, or may include two or more processing units. In addition, the processor may also include a general-purpose processor (CPU) or a graphics processing unit (GPU), a field programmable gate array (FPGA), or an application-specific integrated circuit (ASIC) to set up and operate a neural network. The processor may also include an on-chip memory for caching (including a memory in the processing device).

In some examples, the present disclosure provides a chip which includes the above neural network processor configured to execute the information processing method.

In some examples, the present disclosure provides a chip package structure which includes the above chip.

In some examples, the present disclosure provides a board card which includes the above chip package structure.

In some examples, the present disclosure provides an electronic device which includes the above board card.

The electronic device may include a data processing device, a robot, a computer, a printer, a scanner, a tablet, a smart terminal, a mobile phone, a traffic recorder, a navigator, a sensor, a webcam, a server, a cloud-based server, a camera, a video camera, a projector, a watch, a headphone, a mobile storage, a wearable device, a vehicle, a household appliance, and/or a medical equipment.

The vehicle may include an airplane, a ship, and/or a car. The household electrical appliance may include a television, an air conditioner, a microwave oven, a refrigerator, an electric rice cooker, a humidifier, a washing machine, an electric lamp, a gas cooker, and a range hood. The medical equipment may include a nuclear magnetic resonance spectrometer, a B-ultrasonic scanner, and/or an electrocardiograph.

It should be noted that, the foregoing examples of method, for the sake of conciseness, are all described as a series of action combinations, but those skilled in the art should know that since according to the present disclosure, the steps may be performed in a different order or simultaneously, the disclosure is not limited by the described order of action. Secondly, those skilled in the art should also understand that the examples described in the specification are all optional, and the actions and modules involved are not necessarily required for this disclosure.

In the examples above, the description of each example has its own emphasis. For a part that is not described in detail in one example, reference may be made to related descriptions in other examples.

It should be understood that in the examples provided by the present disclosure, the disclosed device may be implemented in another manner. For instance, the examples above are merely illustrative. For instance, the division of the units is only a logical function division. In a real implementation, there may be another manner for division. For instance, a plurality of units or components may be combined or may be integrated in another system, or some features can be ignored or not performed. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection may be implemented through indirect coupling or communication connection of some interfaces, devices or units, and may be electrical or other forms.

The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units. In other words, the components may be located in one place, or may be distributed to a plurality of network units. According to certain needs, some or all of the units can be selected for realizing the purposes of the examples of the present disclosure.

In addition, the functional units in each example of the present application may be integrated into one processing unit, or each of the units may exist separately and physically, or two or more units may be integrated into one unit. The integrated units above may be implemented in the form of hardware or in the form of software program modules.

When the integrated units are implemented in the form of a software program module and sold or used as an independent product, they may be stored in a computer-readable memory. Based on such understanding, the essence of the technical solutions of the present disclosure, or a part of the present disclosure that contributes to the prior art, or all or part of technical solutions, can all or partly embodied in the form of a software product that is stored in a memory. The software product includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the examples of the present disclosure. The foregoing memory includes: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disc, and other media that can store program codes.

A person of ordinary skill in the art may understand that all or part of the steps of the foregoing examples of method may be completed by a program instructing related hardware. The program may be stored in a computer-readable memory, and the memory may include a flash disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, an optical disk, or the like.

The examples of the present disclosure have been described in detail above. Specific examples have been used in the specification to explain the principles and implementation manners of the present disclosure. The descriptions of the above examples are only used to facilitate understanding of the methods and core ideas of the present disclosure. Persons of ordinary skill in the art may change the implementation and application scope according to the ideas of the present application. In summary, the content of this specification should not be construed as a limitation on the present disclosure. 

What is claimed:
 1. An information processing method applied to a computation circuit, wherein the computation circuit comprises a communication circuit and an operation circuit, and the method comprises: controlling, by the computation circuit, the communication circuit to obtain user data to be processed, wherein the user data is used to recommend product recommendation information of interest to the user; controlling, by the computation circuit, the operation circuit to obtain and call an operation instruction to process the user data to obtain product recommendation information corresponding to the user data, wherein the operation instruction is a preset instruction for product prediction and recommendation, and the product recommendation information comprises at least one target product recommended for the user.
 2. The method of claim 1, wherein the user data includes at least one of the following: user behavior data, user physical sign data, group data, and time data, wherein the user behavior data is used to indicate historical product records of the user, the user's physical sign data is used to indicate physiological data of the user, the group data is used to indicate data of other users within a same geographic area as the user, and the time data is used to indicate time of generation of the user behavior data.
 3. The method of claim 1, wherein the computation circuit further includes a register circuit and a controller circuit, and the controlling, by the computation circuit, the operation circuit to obtain and call an operation instruction to process the user data to obtain product recommendation information corresponding to the user data includes: controlling, by the computation circuit, the controller circuit to fetch an operation instruction associated with a network model from the register circuit, and sending, by the computation circuit, the operation instruction to the operation circuit, and controlling, by the computation circuit, the operation circuit to call an operation instruction associated with the network model to perform feature extraction on the user data to obtain respective recommendation information of a plurality of products, wherein the recommendation information at least includes a product score, and controlling, by the computation circuit, the operation circuit to select recommendation information whose score exceeds a preset threshold from the plurality of recommendation information as the product recommendation information.
 4. The method of claim 3, wherein the neural network model includes any one or more of the following functional layers: a convolution operation layer, a pooling layer, an activation softmax layer, a batch normalization layer, and a fully connected layer, wherein an operation of the convolution operation layer is implemented through a convolution operation instruction, an operation of the pooling layer is implemented through a pooling instruction, an operation of the activation layer is implemented through an activation instruction, an operation of the batch normalization layer is implemented through the batch normalization instruction, and an operation of the fully connected layer is implemented through a fully connected instruction.
 5. The method of claim 3, wherein the computation circuit further includes a data access circuit and a storage medium, and the computation circuit controls the operation circuit to send the product recommendation information to the data access circuit and store the product recommendation information in the storage medium.
 6. The method of claim 1, wherein the user data includes sparse user data which is used to indicate the user's record data with a small amount.
 7. The method of claim 1, wherein the operation circuit includes a primary operation module and a plurality of secondary operation modules, wherein the primary operation module is interconnected with the plurality of secondary operation modules by an interconnection module, and when the operation instruction is a convolution operation instruction, the calling the operation instruction to process the user data includes: controlling, by the computation circuit, the secondary operation modules to implement a convolution operation of input data and a convolution kernel in a convolutional neural network algorithm, wherein the input data is the user data and the convolutional neural network algorithm corresponds to the convolution operation instruction, controlling, by the computation circuit, the interconnection module to implement data transfer between the primary operation module and the secondary operation modules, before a forward operation of a neural network fully connected layer starts, transferring, by the primary operation module, the input data to each secondary operation module through the interconnection module, and after the computation of the secondary operation modules is completed, splicing, by the interconnection module, output scalars of the respective secondary operation modules stage by stage to obtain an intermediate vector, and sending the intermediate vector back to the primary operation module, controlling, by the computation circuit, the primary operation module to splice intermediate vectors corresponding of all input data into an intermediate result for subsequent operations, and controlling, by the computation circuit, the primary operation module to add bias data to the intermediate result, and then performing an activation operation.
 8. (canceled)
 9. The method of claim 7, wherein the primary operation module includes a first operation circuit, wherein the first operation circuit includes a vector addition circuit and an activation circuit, the controlling, by the computation circuit, the primary operation module to add bias data to the intermediate result, and then performing an activation operation include: controlling, by the computation circuit, the vector addition circuit to implement a bias addition operation of a convolutional neural network operation and perform element-wise addition on bias data and the intermediate result to obtain a bias result, and controlling, by the computation circuit, the activation circuit to perform an activation function operation on the bias result.
 10. (canceled)
 11. The method of claim 7, wherein each secondary operation module includes a second operation circuit, wherein the second operation circuit includes a vector multiplication circuit and an accumulation circuit, the controlling, by the computation circuit, the secondary operation modules to perform a convolution operation of input data and a convolution kernel in a convolutional neural network algorithm includes: controlling, by the computation circuit, the vector multiplication circuit to perform a vector multiplication operation of the convolution operation, and controlling, by the computation circuit, the accumulation circuit to perform an accumulation operation of the convolution operation.
 12. (canceled)
 13. (canceled)
 14. The method of claim 7, wherein the computation circuit controls the plurality of secondary operation modules to compute respective output scalars in parallel by using the same input data and respective convolution kernels.
 15. A computation circuit, comprising a communication circuit and an operation circuit, wherein the communication circuit is configured to obtain user data to be processed, wherein the user data is used to recommend product recommendation information of interest to the user; the operation circuit is configured to obtain and call an operation instruction to process the user data to obtain product recommendation information corresponding to the user data, wherein the operation instruction is a preset instruction for product prediction and recommendation, and the product recommendation information comprises at least one target product recommended for the user.
 16. The computation circuit of claim 15, further comprising a register circuit and a controller circuit, wherein the controller circuit is configured to fetch an operation instruction associated with a network model from the register circuit, and send the operation instruction to the operation circuit, the operation circuit is configured to call an operation instruction associated with the network model to perform feature extraction on the user data to obtain respective recommendation information of a plurality of products, wherein the recommendation information at least includes a score of the product, and the operation circuit is configured to select recommendation information whose score exceeds a preset threshold from the plurality of recommendation information as the product recommendation information.
 17. The computation circuit of claim 15, wherein the operation circuit includes a primary operation module and a plurality of secondary operation modules, wherein the primary operation module is interconnected with the plurality of secondary operation modules by an interconnection module, and when the operation instruction is a convolution operation instruction, the secondary operation modules are configured to implement a convolution operation of input data and a convolution kernel in a convolutional neural network algorithm, wherein the input data is the user data and the convolutional neural network algorithm corresponds to the convolution operation instruction, the interconnection module is configured to implement data transfer between the primary operation module and the secondary operation modules, before a forward operation of a neural network fully connected layer starts, the primary operation module sends the input data to each secondary operation module through the interconnection module, and after the computation of the secondary operation modules is completed, the interconnection module splices output scalars of the respective secondary operation modules stage by stage into an intermediate vector and sends the intermediate vector back to the primary operation module, and the primary operation module is configured to splice intermediate vectors corresponding to all input data into an intermediate result, and perform subsequent operations on the intermediate result, wherein the primary operation module is configured to add bias data to the intermediate result, and then perform an activation operation.
 18. The computation circuit of claim 17, wherein the primary operation module includes a first operation circuit, wherein the first operation circuit includes a vector addition circuit and an activation circuit, the vector addition circuit is configured to implement a bias addition operation of a convolutional neural network operation and perform element-wise addition on bias data and the intermediate result to obtain a bias result, and the activation circuit is configured to perform an activation function operation on the bias result, and wherein each secondary operation module includes a second operation circuit, wherein the second operation circuit includes a vector multiplication circuit and an accumulation circuit, the vector multiplication circuit is configured to perform a vector multiplication operation of a convolution operation, and the accumulation circuit is configured to perform an accumulation operation of the convolution operation.
 19. (canceled)
 20. (canceled)
 21. (canceled)
 22. The computation circuit of claim 17, wherein the plurality of secondary modules use the same input data and respective convolution kernels to compute respective output scalars in parallel.
 23. (canceled)
 24. (canceled)
 25. (canceled) 